arXiv：cs.LG· Qirun Zeng, Manhin Poon, Xiangxiang Dai, Qixin Zhang, Jinhang Zuo·· 4 小时前AI 评分31
超越奖励压制：有界奖励热启动 Bandits 的近最优离线攻击
Beyond Reward Suppression: Near-Optimal Offline Attacks on Warm-Start Bandits with Bounded Rewards
AI 导读
研究揭示了对热启动 Bandits 的离线攻击中，目标推广并非启发式手段：当目标臂靠近奖励下界时，任何针对 UCB 的近最优成本攻击都必须将不可忽略比例的成本分配给目标臂，才能使其在几乎所有在线轮次中被选中。
正文
Abstract:Adversarial attacks on bandits aim to mislead a learner toward a target arm while keeping the attack cost small. Existing attacks typically achieve this by suppressing non-target arms. In practice, however, manipulation such as fake reviews often directly promotes the target item. We study this gap through bounded offline attacks on warm-start bandits, where an attacker can inject only valid action-reward pairs into the warm-start history before deployment. We show that target promotion is not merely a heuristic: when the target arm lies near the lower reward boundary, any order-optimal-cost attack against UCB that makes it selected in nearly all online rounds must allocate a nonvanishing fraction of its cost to the target arm. We then design an attack that achieves the optimal sublinear cost and characterize its allocation between target promotion and non-target suppression. We further extend the attack to Thompson Sampling, $\epsilon$-greedy, and a broader class of bandit algorithms. Experiments on real-world and synthetic data validate the effectiveness of our attacks.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.10000 [cs.LG] |
| (or arXiv:2610.10000v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10000 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qirun Zeng [view email]
[v1]
Wed, 7 Oct 2026 12:57:18 UTC (62 KB)
来源：arXiv：cs.LG · arxiv.org